Multifaceted Regulation of Translational Readthrough by RNA Replication Elements in a Tombusvirus
Bibliographic record
Abstract
Translational readthrough of stop codons by ribosomes is a recoding event used by a variety of viruses, including plus-strand RNA tombusviruses. Translation of the viral RNA-dependent RNA polymerase (RdRp) in tombusviruses is mediated using this strategy and we have investigated this process using a variety of in vitro and in vivo approaches. Our results indicate that readthrough generating the RdRp requires a novel long-range RNA-RNA interaction, spanning a distance of ∼3.5 kb, which occurs between a large RNA stem-loop located 3'-proximal to the stop codon and an RNA replication structure termed RIV at the 3'-end of the viral genome. Interestingly, this long-distance RNA-RNA interaction is modulated by mutually-exclusive RNA structures in RIV that represent a type of RNA switch. Moreover, a different long-range RNA-RNA interaction that was previously shown to be necessary for viral RNA replicase assembly was also required for efficient readthrough production of the RdRp. Accordingly, multiple replication-associated RNA elements are involved in modulating the readthrough event in tombusviruses and we propose an integrated mechanistic model to describe how this regulatory network could be advantageous by (i) providing a quality control system for culling truncated viral genomes at an early stage in the replication process, (ii) mediating cis-preferential replication of viral genomes, and (iii) coordinating translational readthrough of the RdRp with viral genome replication. Based on comparative sequence analysis and experimental data, basic elements of this regulatory model extend to other members of Tombusviridae, as well as to viruses outside of this family.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".